Development of a Robust Cooperative Adaptive Cruise Control With Dynamic Topology

Cui, Lian; Chen, Zheng; Wang, Aobo; Hu, Jia; Park, Byungkyu Brian

doi:10.1109/tits.2020.3043194

Cited by 20 publications

(10 citation statements)

References 30 publications

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“…That may be caused by the sensitivity in the selection of the most appropriate leader in the case of heterogeneous platoons. Dynamic topologies might be needed to optimize the effectiveness of CACC, as discussed in Cui et al ( 48 ).…”

Section: Discussionmentioning

confidence: 99%

Multianticipative Adaptive Cruise Control Compared With Connectivity-Enhanced Solutions: Simulation-Based Investigation in Mixed Traffic Platoons

Donà

Mattas

Albano³

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The paper explores via simulation analysis how multianticipative adaptive cruise control (M-ACC) can address traffic oscillations with respect to cooperative adaptive cruise control (CACC) approaches. The work is grounded on our previous findings where we suggested a functioning logic and a performance characterization of M-ACC. That activity followed a manufacturer’s claim that vehicles capable of reacting to more than one leader ahead are already populating public roads. In this light, M-ACC represents an intermediate solution between the traditional ACC, which typically is capable of reacting to only one vehicle ahead, and connectivity-enhanced approaches, which in principle could track multiple leaders. Given that M-ACC is effective from small penetration rates—as it does not require the presence of other M-ACC-equipped vehicles to reach its full potential—performance comparison with CACC is not straightforward. In particular, for a fair comparison it is necessary to quantify the performance gap as a function of the penetration rate, which is exactly the objective of this paper. Three CACC topologies are investigated and both stochastic simulations and Pareto analyses are performed. The results demonstrate that, for small market penetration figures, M-ACC can still guarantee better performances than any CACC heterogenous platoons for all the metrics considered given that it does not require infrastructure or other adjacent vehicles featuring the same technology. Considering that the market adoption of connectivity-based solutions is facing challenging practical issues, M-ACC could represent a suitable as well as realistic option to harvest the benefits of automation to traffic flow in the near future.

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Section: Discussionmentioning

confidence: 99%

Multianticipative Adaptive Cruise Control Compared With Connectivity-Enhanced Solutions: Simulation-Based Investigation in Mixed Traffic Platoons

Donà

Mattas

Albano³

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…CCAM mainly includes the technology of vehicular communications, automated driving, and cooperating transportation systems. Recently, numerical research proposed a series of innovative technology for CCAM, such as 5G-SDN-MPTCP-based communication systems [45], multi-tier orchestration platform architectures [46], safety-related assessment platforms [47], and a significant number of cooperative adaptive cruising control algorithms [48,49]. There are also some evaluation and incentive systems related to the social and economic benefits of CCAM.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data

Wang

2022

Algorithms

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Capacity drop is the critical phenomenon that triggers traffic congestion, while traffic evolution is very complex during a capacity drop. This study applied the empirical vehicle trajectory data to explore the traffic characteristics during the capacity drop at the tunnel bottleneck section. We first construct a capacity drop analysis model using image processing technology to extract high-precision vehicle trajectories. We then analyze the characteristics of the evolution process of the capacity drop at the bottleneck area. The results show that the capacity drop is a dynamic evolution process from free flow to congested flow where traffic operation is distinct. The capacity drop shows the difference between congested flow and non-congested flow. The driving characteristics of drivers in the two states are also different. The influence of lane-changing behavior on the capacity drop is estimated. In the free flow state, the disturbance caused by lane-changing can be quickly eliminated. With the increase in vehicle numbers in the area, the frequent lane-changing behavior accumulates disturbance. When the disturbance reaches a certain degree, congestion will occur, and the vehicle’s speed will drop sharply, resulting in a capacity drop.

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“…They observed their algorithm to improve the plausibility checks by 20%. Another study [17] analyzed a Cooperative Adaptive Cruise Control (CACC) platoon of six vehicles against time-delay attacks and observed their CACC algorithm to be stable against attacks with no cruise or jerks.…”

Section: Literature Reviewmentioning

confidence: 99%

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Wang,

Li,

Khattak

2024

Electronics

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Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

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Development of a Robust Cooperative Adaptive Cruise Control With Dynamic Topology

Cited by 20 publications

References 30 publications

Multianticipative Adaptive Cruise Control Compared With Connectivity-Enhanced Solutions: Simulation-Based Investigation in Mixed Traffic Platoons

Multianticipative Adaptive Cruise Control Compared With Connectivity-Enhanced Solutions: Simulation-Based Investigation in Mixed Traffic Platoons

Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

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